Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Introduction to R01:11

Introduction to R

4.2K
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
4.2K
Light Acquisition02:16

Light Acquisition

9.3K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
9.3K
Multiple Regression01:25

Multiple Regression

3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K
Levels of Use of a GIS01:29

Levels of Use of a GIS

339
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
339
Interpreting R Charts01:22

Interpreting R Charts

326
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
326
Manipulation and Analysis01:21

Manipulation and Analysis

282
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
282

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Chronic and Acute Effects of Imidacloprid on a Simulated BEEHAVE Honeybee Colony.

Environmental toxicology and chemistry·2022
Same author

Extinction and Spatial Structure in Simulation Models.

Conservation biology : the journal of the Society for Conservation Biology·2022
Same author

Revisiting Theron's hypothesis on the origin of fairy circles after four decades: Euphorbias are not the cause.

BMC ecology and evolution·2021
Same author

Integrating human behavior and snake ecology with agent-based models to predict snakebite in high risk landscapes.

PLoS neglected tropical diseases·2021
Same author

Lack of astrocytes hinders parenchymal oligodendrocyte precursor cells from reaching a myelinating state in osmolyte-induced demyelination.

Acta neuropathologica communications·2020
Same author

EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics.

PloS one·2019
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Jan 12, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

ALGR: A multi-purpose agricultural landscape generator in R.

Eyal Goldstein1, Antonia Deutscher1,2, Eamon O'Keeffe1

  • 1University of Göttingen, Ecosystem Modelling, Göttingen, Germany.

Plos One
|October 30, 2025
PubMed
Summary
This summary is machine-generated.

Agricultural landscape generators (ALG) create dynamic maps for ecological models. ALGR is a new, adaptable tool that integrates with R, offering realistic spatial patterns for diverse applications.

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.4K

Related Experiment Videos

Last Updated: Jan 12, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.4K

Area of Science:

  • Ecological modeling
  • Agricultural landscape simulation
  • Spatial analysis

Background:

  • Spatially explicit ecological models require dynamic landscape maps for accurate simulations.
  • Existing agricultural landscape generators (ALG) are often limited by being static, non-general, or overly simplified.
  • There is a need for flexible, adaptable tools to generate realistic agricultural landscapes for modeling.

Purpose of the Study:

  • To introduce ALGR, a novel, general-purpose, dynamic agricultural landscape generator.
  • To provide a tool that balances structural realism with adaptability for diverse applications.
  • To enable seamless integration within the R programming and r-spatial package environments.

Main Methods:

  • ALGR employs a three-step approach: outlining potential space, field placement, and landscape enrichment.
  • The generator is designed for adaptability to diverse regions and applications.
  • Integration with the R programming environment and r-spatial packages is a key feature.

Main Results:

  • ALGR generates agricultural landscapes with realistic spatial patterns.
  • The tool demonstrates flexibility through examples including land use share simulation and parameter tuning.
  • It facilitates the recreation of real-world landscape patterns and spatial distribution of crop portfolios.

Conclusions:

  • ALGR is the first ALG specifically designed for easy integration within the R environment.
  • Its general-purpose nature and adaptability make it a valuable tool for ecological and agricultural modelers.
  • ALGR simplifies the integration of dynamic landscape generation into modeling workflows.